{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "2bebf17b",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-05-24T03:29:25.412264Z",
     "iopub.status.busy": "2023-05-24T03:29:25.411901Z",
     "iopub.status.idle": "2023-05-24T03:29:40.868707Z",
     "shell.execute_reply": "2023-05-24T03:29:40.867743Z"
    },
    "executionInfo": {
     "elapsed": 2844,
     "status": "ok",
     "timestamp": 1684409199330,
     "user": {
      "displayName": "RUIYING CHEN",
      "userId": "09384040230747805046"
     },
     "user_tz": -480
    },
    "id": "wHsFZo6akD8M",
    "outputId": "d017ea14-1ff6-429f-d7da-2f6ad9e82ec1",
    "papermill": {
     "duration": 15.469767,
     "end_time": "2023-05-24T03:29:40.871136",
     "exception": false,
     "start_time": "2023-05-24T03:29:25.401369",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "import torch\n",
    "import torchvision\n",
    "import torchvision.transforms as transforms\n",
    "\n",
    "transform = transforms.Compose([transforms.ToTensor(), transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))])\n",
    "batch_size = 8\n",
    "\n",
    "device=\"cuda\"\n",
    "\n",
    "#uncomment when using colab or local cpu\n",
    "# train_dataset = torchvision.datasets.cifar.CIFAR100(root='cifar100', train=True, transform=transform, download=True)\n",
    "# test_dataset = torchvision.datasets.cifar.CIFAR100(root='cifar100', train=False, transform=transform, download=True)\n",
    "# train_loader = torch.utils.data.DataLoader(train_dataset, batch_size=batch_size, shuffle=True, num_workers=2)\n",
    "# test_loader = torch.utils.data.DataLoader(test_dataset, batch_size=batch_size, shuffle=False, num_workers=2)\n",
    "\n",
    "#uncomment when using kaggle\n",
    "\n",
    "train_dataset = torchvision.datasets.ImageFolder(\"/kaggle/input/cifar100/CIFAR100/TRAIN\", transform=transform)\n",
    "\n",
    "test_dataset = torchvision.datasets.ImageFolder(\"/kaggle/input/cifar100/CIFAR100/TEST\", transform=transform)\n",
    "\n",
    "train_loader = torch.utils.data.DataLoader(train_dataset, batch_size=batch_size, shuffle=True, num_workers=2)\n",
    "\n",
    "test_loader = torch.utils.data.DataLoader(test_dataset, batch_size=batch_size, shuffle=False, num_workers=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "7b7806b8",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-05-24T03:29:40.883686Z",
     "iopub.status.busy": "2023-05-24T03:29:40.883156Z",
     "iopub.status.idle": "2023-05-24T03:29:41.280346Z",
     "shell.execute_reply": "2023-05-24T03:29:41.279212Z"
    },
    "executionInfo": {
     "elapsed": 8,
     "status": "ok",
     "timestamp": 1684409199330,
     "user": {
      "displayName": "RUIYING CHEN",
      "userId": "09384040230747805046"
     },
     "user_tz": -480
    },
    "id": "VjkCK8NykPMb",
    "outputId": "d8b6db1d-275c-4979-f5c6-411c763dea3f",
    "papermill": {
     "duration": 0.406496,
     "end_time": "2023-05-24T03:29:41.283462",
     "exception": false,
     "start_time": "2023-05-24T03:29:40.876966",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "torch.Size([8])\n"
     ]
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "# functions to show an image\n",
    "\n",
    "\n",
    "def imshow(img):\n",
    "    img = img / 2 + 0.5     # unnormalize\n",
    "    npimg = img.numpy()\n",
    "    plt.imshow(np.transpose(npimg, (1, 2, 0)))\n",
    "    plt.show()\n",
    "\n",
    "\n",
    "# get some random training images\n",
    "dataiter = iter(train_loader)\n",
    "# images, labels = dataiter.next()\n",
    "images, labels = next(dataiter)\n",
    "\n",
    "# show images\n",
    "imshow(torchvision.utils.make_grid(images))\n",
    "# print labels\n",
    "print(labels.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "b7ced396",
   "metadata": {
    "execution": {
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    "executionInfo": {
     "elapsed": 7,
     "status": "ok",
     "timestamp": 1684409199331,
     "user": {
      "displayName": "RUIYING CHEN",
      "userId": "09384040230747805046"
     },
     "user_tz": -480
    },
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   "outputs": [],
   "source": [
    "import math\n",
    "\n",
    "import torch\n",
    "import torch.nn as nn\n",
    "import torch.nn.functional as F\n",
    "from torch.nn import init\n",
    "\n",
    "def conv3x3(in_planes, out_planes, stride=1):\n",
    "    \"3x3 convolution with padding\"\n",
    "    return nn.Conv2d(\n",
    "        in_planes, out_planes, kernel_size=3, stride=stride, padding=1, bias=False\n",
    "    )\n",
    "\n",
    "\n",
    "class BasicBlock(nn.Module):\n",
    "    expansion = 1\n",
    "\n",
    "    def __init__(\n",
    "        self, inplanes, planes, stride=1, downsample=None, use_triplet_attention=False\n",
    "    ):\n",
    "        super(BasicBlock, self).__init__()\n",
    "        self.conv1 = conv3x3(inplanes, planes, stride)\n",
    "        self.bn1 = nn.BatchNorm2d(planes)\n",
    "        self.relu = nn.ReLU(inplace=True)\n",
    "        self.conv2 = conv3x3(planes, planes)\n",
    "        self.bn2 = nn.BatchNorm2d(planes)\n",
    "        self.downsample = downsample\n",
    "        self.stride = stride\n",
    "\n",
    "        if use_triplet_attention:\n",
    "            self.triplet_attention = TripletAttention(planes, 16)\n",
    "        else:\n",
    "            self.triplet_attention = None\n",
    "\n",
    "    def forward(self, x):\n",
    "        residual = x\n",
    "\n",
    "        out = self.conv1(x)\n",
    "        out = self.bn1(out)\n",
    "        out = self.relu(out)\n",
    "\n",
    "        out = self.conv2(out)\n",
    "        out = self.bn2(out)\n",
    "\n",
    "        if self.downsample is not None:\n",
    "            residual = self.downsample(x)\n",
    "\n",
    "        if not self.triplet_attention is None:\n",
    "            out = self.triplet_attention(out)\n",
    "\n",
    "        out += residual\n",
    "        out = self.relu(out)\n",
    "\n",
    "        return out\n",
    "\n",
    "\n",
    "class Bottleneck(nn.Module):\n",
    "    expansion = 4\n",
    "\n",
    "    def __init__(\n",
    "        self, inplanes, planes, stride=1, downsample=None, use_triplet_attention=False\n",
    "    ):\n",
    "        super(Bottleneck, self).__init__()\n",
    "        self.conv1 = nn.Conv2d(inplanes, planes, kernel_size=1, bias=False)\n",
    "        self.bn1 = nn.BatchNorm2d(planes)\n",
    "        self.conv2 = nn.Conv2d(\n",
    "            planes, planes, kernel_size=3, stride=stride, padding=1, bias=False\n",
    "        )\n",
    "        self.bn2 = nn.BatchNorm2d(planes)\n",
    "        self.conv3 = nn.Conv2d(planes, planes * 4, kernel_size=1, bias=False)\n",
    "        self.bn3 = nn.BatchNorm2d(planes * 4)\n",
    "        self.relu = nn.ReLU(inplace=True)\n",
    "        self.downsample = downsample\n",
    "        self.stride = stride\n",
    "\n",
    "        if use_triplet_attention:\n",
    "            self.triplet_attention = TripletAttention(planes * 4, 16)\n",
    "        else:\n",
    "            self.triplet_attention = None\n",
    "\n",
    "    def forward(self, x):\n",
    "        residual = x\n",
    "\n",
    "        out = self.conv1(x)\n",
    "        out = self.bn1(out)\n",
    "        out = self.relu(out)\n",
    "\n",
    "        out = self.conv2(out)\n",
    "        out = self.bn2(out)\n",
    "        out = self.relu(out)\n",
    "\n",
    "        out = self.conv3(out)\n",
    "        out = self.bn3(out)\n",
    "\n",
    "        if self.downsample is not None:\n",
    "            residual = self.downsample(x)\n",
    "\n",
    "        if not self.triplet_attention is None:\n",
    "            out = self.triplet_attention(out)\n",
    "\n",
    "        out += residual\n",
    "        out = self.relu(out)\n",
    "\n",
    "        return out\n",
    "\n",
    "\n",
    "class ResNet(nn.Module):\n",
    "    def __init__(self, block, layers, network_type, num_classes, att_type=None):\n",
    "        self.inplanes = 64\n",
    "        super(ResNet, self).__init__()\n",
    "        self.network_type = network_type\n",
    "        # different model config between ImageNet and CIFAR\n",
    "        if network_type == \"ImageNet\":\n",
    "            self.conv1 = nn.Conv2d(\n",
    "                3, 64, kernel_size=7, stride=2, padding=3, bias=False\n",
    "            )\n",
    "            self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)\n",
    "            self.avgpool = nn.AvgPool2d(7)\n",
    "        else:\n",
    "            self.conv1 = nn.Conv2d(\n",
    "                3, 64, kernel_size=3, stride=1, padding=1, bias=False\n",
    "            )\n",
    "\n",
    "        self.bn1 = nn.BatchNorm2d(64)\n",
    "        self.relu = nn.ReLU(inplace=True)\n",
    "\n",
    "        self.layer1 = self._make_layer(block, 64, layers[0], att_type=att_type)\n",
    "        self.layer2 = self._make_layer(\n",
    "            block, 128, layers[1], stride=2, att_type=att_type\n",
    "        )\n",
    "        self.layer3 = self._make_layer(\n",
    "            block, 256, layers[2], stride=2, att_type=att_type\n",
    "        )\n",
    "        self.layer4 = self._make_layer(\n",
    "            block, 512, layers[3], stride=2, att_type=att_type\n",
    "        )\n",
    "\n",
    "        self.fc = nn.Linear(512 * block.expansion, num_classes)\n",
    "\n",
    "        init.kaiming_normal_(self.fc.weight)\n",
    "        for key in self.state_dict():\n",
    "            if key.split(\".\")[-1] == \"weight\":\n",
    "                if \"conv\" in key:\n",
    "                    init.kaiming_normal_(self.state_dict()[key], mode=\"fan_out\")\n",
    "                if \"bn\" in key:\n",
    "                    if \"SpatialGate\" in key:\n",
    "                        self.state_dict()[key][...] = 0\n",
    "                    else:\n",
    "                        self.state_dict()[key][...] = 1\n",
    "            elif key.split(\".\")[-1] == \"bias\":\n",
    "                self.state_dict()[key][...] = 0\n",
    "\n",
    "    def _make_layer(self, block, planes, blocks, stride=1, att_type=None):\n",
    "        downsample = None\n",
    "        if stride != 1 or self.inplanes != planes * block.expansion:\n",
    "            downsample = nn.Sequential(\n",
    "                nn.Conv2d(\n",
    "                    self.inplanes,\n",
    "                    planes * block.expansion,\n",
    "                    kernel_size=1,\n",
    "                    stride=stride,\n",
    "                    bias=False,\n",
    "                ),\n",
    "                nn.BatchNorm2d(planes * block.expansion),\n",
    "            )\n",
    "\n",
    "        layers = []\n",
    "        layers.append(\n",
    "            block(\n",
    "                self.inplanes,\n",
    "                planes,\n",
    "                stride,\n",
    "                downsample,\n",
    "                use_triplet_attention=att_type == \"TripletAttention\",\n",
    "            )\n",
    "        )\n",
    "        self.inplanes = planes * block.expansion\n",
    "        for i in range(1, blocks):\n",
    "            layers.append(\n",
    "                block(\n",
    "                    self.inplanes,\n",
    "                    planes,\n",
    "                    use_triplet_attention=att_type == \"TripletAttention\",\n",
    "                )\n",
    "            )\n",
    "\n",
    "        return nn.Sequential(*layers)\n",
    "\n",
    "    def forward(self, x):\n",
    "        x = self.conv1(x)\n",
    "        x = self.bn1(x)\n",
    "        x = self.relu(x)\n",
    "        if self.network_type == \"ImageNet\":\n",
    "            x = self.maxpool(x)\n",
    "\n",
    "        x = self.layer1(x)\n",
    "        x = self.layer2(x)\n",
    "        x = self.layer3(x)\n",
    "        x = self.layer4(x)\n",
    "\n",
    "        if self.network_type == \"ImageNet\":\n",
    "            x = self.avgpool(x)\n",
    "        else:\n",
    "            x = F.avg_pool2d(x, 4)\n",
    "        x = x.view(x.size(0), -1)\n",
    "        x = self.fc(x)\n",
    "        return x\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a5b264f7",
   "metadata": {
    "papermill": {
     "duration": 0.005891,
     "end_time": "2023-05-24T03:29:41.346172",
     "exception": false,
     "start_time": "2023-05-24T03:29:41.340281",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "## 多尺度 attention trial"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "103c1bf1",
   "metadata": {
    "execution": {
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "import torch\n",
    "import torch.nn as nn\n",
    "\n",
    "class MultiScaleAttention(nn.Module):\n",
    "    def __init__(self, in_channels, scales=[1, 2, 3]):\n",
    "        super(MultiScaleAttention, self).__init__()\n",
    "        self.scales = scales\n",
    "        \n",
    "        self.attention_layers = nn.ModuleList()\n",
    "        for scale in scales:\n",
    "            self.attention_layers.append(\n",
    "                nn.Sequential(\n",
    "                    nn.Conv2d(in_channels, in_channels, kernel_size=3, padding=1, stride=scale),\n",
    "                    nn.BatchNorm2d(in_channels),\n",
    "                    nn.ReLU(inplace=True),\n",
    "                    nn.Conv2d(in_channels, in_channels, kernel_size=3, padding=1, stride=1),\n",
    "                    nn.BatchNorm2d(in_channels),\n",
    "                    nn.Sigmoid()\n",
    "                )\n",
    "            )\n",
    "\n",
    "    def forward(self, x):\n",
    "        attention_maps = []\n",
    "        for i, attention_layer in enumerate(self.attention_layers):\n",
    "            attention_map = attention_layer(x)\n",
    "            attention_map = nn.functional.interpolate(attention_map, size=x.size()[2:], mode='bilinear', align_corners=False)\n",
    "            attention_maps.append(attention_map)\n",
    "\n",
    "        out = torch.mean(torch.stack(attention_maps), dim=0)\n",
    "        out = out * x\n",
    "\n",
    "        return out\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5bcfb8c4",
   "metadata": {
    "papermill": {
     "duration": 0.005905,
     "end_time": "2023-05-24T03:29:41.382170",
     "exception": false,
     "start_time": "2023-05-24T03:29:41.376265",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "## 定义SKNet"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "c3546b47",
   "metadata": {
    "execution": {
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   "outputs": [],
   "source": [
    "\n",
    "#sknet\n",
    "class SKConv(nn.Module):\n",
    "    def __init__(self, features, M=2, G=32, r=16, stride=1 ,L=32):\n",
    "        \"\"\" Constructor\n",
    "        Args:\n",
    "            features: input channel dimensionality.\n",
    "            M: the number of branchs.\n",
    "            G: num of convolution groups.\n",
    "            r: the ratio for compute d, the length of z.\n",
    "            stride: stride, default 1.\n",
    "            L: the minimum dim of the vector z in paper, default 32.\n",
    "        \"\"\"\n",
    "        super(SKConv, self).__init__()\n",
    "        d = max(int(features/r), L)\n",
    "        self.M = M\n",
    "        self.features = features\n",
    "        self.convs = nn.ModuleList([])\n",
    "        for i in range(M):\n",
    "            self.convs.append(nn.Sequential(\n",
    "                nn.Conv2d(features, features, kernel_size=3, stride=stride, padding=1+i, dilation=1+i, groups=G, bias=False),\n",
    "                nn.BatchNorm2d(features),\n",
    "                nn.ReLU(inplace=True)\n",
    "            ))\n",
    "        self.gap = nn.AdaptiveAvgPool2d((1,1))\n",
    "        self.fc = nn.Sequential(nn.Conv2d(features, d, kernel_size=1, stride=1, bias=False),\n",
    "                                nn.BatchNorm2d(d),\n",
    "                                nn.ReLU(inplace=True))\n",
    "        self.fcs = nn.ModuleList([])\n",
    "        for i in range(M):\n",
    "            self.fcs.append(\n",
    "                 nn.Conv2d(d, features, kernel_size=1, stride=1)\n",
    "            )\n",
    "        self.softmax = nn.Softmax(dim=1)\n",
    "        \n",
    "    def forward(self, x):\n",
    "        \n",
    "        batch_size = x.shape[0]\n",
    "        \n",
    "        feats = [conv(x) for conv in self.convs]      \n",
    "        feats = torch.cat(feats, dim=1)\n",
    "        feats = feats.view(batch_size, self.M, self.features, feats.shape[2], feats.shape[3])\n",
    "        \n",
    "        feats_U = torch.sum(feats, dim=1)\n",
    "        feats_S = self.gap(feats_U)\n",
    "        feats_Z = self.fc(feats_S)\n",
    "\n",
    "        attention_vectors = [fc(feats_Z) for fc in self.fcs]\n",
    "        attention_vectors = torch.cat(attention_vectors, dim=1)\n",
    "        attention_vectors = attention_vectors.view(batch_size, self.M, self.features, 1, 1)\n",
    "        attention_vectors = self.softmax(attention_vectors)\n",
    "        \n",
    "        feats_V = torch.sum(feats*attention_vectors, dim=1)\n",
    "        \n",
    "        return feats_V\n",
    "\n",
    "\n",
    "class SKUnit(nn.Module):\n",
    "    def __init__(self, in_features, mid_features, out_features, M=2, G=32, r=16, stride=1, L=32):\n",
    "        \"\"\" Constructor\n",
    "        Args:\n",
    "            in_features: input channel dimensionality.\n",
    "            out_features: output channel dimensionality.\n",
    "            M: the number of branchs.\n",
    "            G: num of convolution groups.\n",
    "            r: the ratio for compute d, the length of z.\n",
    "            mid_features: the channle dim of the middle conv with stride not 1, default out_features/2.\n",
    "            stride: stride.\n",
    "            L: the minimum dim of the vector z in paper.\n",
    "        \"\"\"\n",
    "        super(SKUnit, self).__init__()\n",
    "        \n",
    "        self.conv1 = nn.Sequential(\n",
    "            nn.Conv2d(in_features, mid_features, 1, stride=1, bias=False),\n",
    "            nn.BatchNorm2d(mid_features),\n",
    "            nn.ReLU(inplace=True)\n",
    "            )\n",
    "        \n",
    "        self.conv2_sk = SKConv(mid_features, M=M, G=G, r=r, stride=stride, L=L)\n",
    "        \n",
    "        self.conv3 = nn.Sequential(\n",
    "            nn.Conv2d(mid_features, out_features, 1, stride=1, bias=False),\n",
    "            nn.BatchNorm2d(out_features)\n",
    "            )\n",
    "        \n",
    "\n",
    "        if in_features == out_features: # when dim not change, input_features could be added diectly to out\n",
    "            self.shortcut = nn.Sequential()\n",
    "        else: # when dim not change, input_features should also change dim to be added to out\n",
    "            self.shortcut = nn.Sequential(\n",
    "                nn.Conv2d(in_features, out_features, 1, stride=stride, bias=False),\n",
    "                nn.BatchNorm2d(out_features)\n",
    "            )\n",
    "        \n",
    "        self.relu = nn.ReLU(inplace=True)\n",
    "    \n",
    "    def forward(self, x):\n",
    "        residual = x\n",
    "        \n",
    "        out = self.conv1(x)\n",
    "        out = self.conv2_sk(out)\n",
    "        out = self.conv3(out)\n",
    "        \n",
    "        return self.relu(out + self.shortcut(residual))\n",
    "\n",
    "class SKNet(nn.Module):\n",
    "    def __init__(self, class_num, nums_block_list = [3, 4, 6, 3], strides_list = [1, 2, 2, 2]):\n",
    "        super(SKNet, self).__init__()\n",
    "        self.basic_conv = nn.Sequential(\n",
    "            nn.Conv2d(3, 64, 7, 2, 3, bias=False),\n",
    "            nn.BatchNorm2d(64),\n",
    "            nn.ReLU(inplace=True),\n",
    "        )\n",
    "        \n",
    "        self.maxpool = nn.MaxPool2d(3,2,1)\n",
    "        \n",
    "        self.stage_1 = self._make_layer(64, 64, 64, nums_block=nums_block_list[0], stride=strides_list[0])\n",
    "        self.multi_scale_attention_1 = MultiScaleAttention(64)\n",
    "        self.stage_2 = self._make_layer(64, 128, 128, nums_block=nums_block_list[1], stride=strides_list[1])\n",
    "        self.multi_scale_attention_2 = MultiScaleAttention(128)\n",
    "        self.stage_3 = self._make_layer(128, 256, 256, nums_block=nums_block_list[2], stride=strides_list[2])\n",
    "        self.multi_scale_attention_3 = MultiScaleAttention(256)\n",
    "        self.stage_4 = self._make_layer(256, 512, 512, nums_block=nums_block_list[3], stride=strides_list[3])\n",
    "        self.multi_scale_attention_4 = MultiScaleAttention(512)\n",
    "        self.gap = nn.AdaptiveAvgPool2d((1, 1))\n",
    "        self.classifier = nn.Linear(512, class_num)\n",
    "        \n",
    "    def _make_layer(self, in_feats, mid_feats, out_feats, nums_block, stride=1):\n",
    "        layers=[SKUnit(in_feats, mid_feats, out_feats, stride=stride)]\n",
    "        for _ in range(1,nums_block):\n",
    "            layers.append(SKUnit(out_feats, mid_feats, out_feats))\n",
    "        return nn.Sequential(*layers)\n",
    "\n",
    "    def forward(self, x):\n",
    "        fea = self.basic_conv(x)\n",
    "        fea = self.maxpool(fea)\n",
    "        fea = self.stage_1(fea)\n",
    "        fea = self.multi_scale_attention_1(fea)\n",
    "        fea = self.stage_2(fea)\n",
    "        fea = self.multi_scale_attention_2(fea)\n",
    "        fea = self.stage_3(fea)\n",
    "        fea = self.multi_scale_attention_3(fea)\n",
    "        fea = self.stage_4(fea)\n",
    "        fea = self.multi_scale_attention_4(fea)\n",
    "        fea = self.gap(fea)\n",
    "        fea = torch.squeeze(fea)\n",
    "        fea = self.classifier(fea)\n",
    "        return fea\n",
    "\n",
    "def SKNet26(nums_class=100):\n",
    "    return SKNet(nums_class, [2, 2, 2, 2])\n",
    "def SKNet50(nums_class=100):\n",
    "    return SKNet(nums_class, [3, 4, 6, 3])\n",
    "def SKNet101(nums_class=100):\n",
    "    return SKNet(nums_class, [3, 4, 23, 3])\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "2ffdca0c",
   "metadata": {
    "execution": {
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     "shell.execute_reply": "2023-05-24T03:29:44.663044Z"
    },
    "executionInfo": {
     "elapsed": 680,
     "status": "ok",
     "timestamp": 1684414637992,
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      "displayName": "RUIYING CHEN",
      "userId": "09384040230747805046"
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    "id": "otA9CIgdnXfW",
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     "status": "completed"
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    "tags": []
   },
   "outputs": [],
   "source": [
    "\n",
    "net=SKNet26().to(device)\n",
    "#net=ResidualNet(\"CIFAR100\",50,100,None).to(device)\n",
    "#print(net)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "2e591b1d",
   "metadata": {
    "execution": {
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    },
    "executionInfo": {
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      "displayName": "RUIYING CHEN",
      "userId": "09384040230747805046"
     },
     "user_tz": -480
    },
    "id": "35VI01P_kqal",
    "outputId": "64747940-f10a-4982-df2f-d5bc5a500b78",
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     "duration": 0.017918,
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     "exception": false,
     "start_time": "2023-05-24T03:29:44.673074",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5000\n"
     ]
    }
   ],
   "source": [
    "import torch.optim as optim\n",
    "\n",
    "criterion = nn.CrossEntropyLoss()\n",
    "optimizer = torch.optim.Adam(net.parameters(), lr=0.0005)\n",
    "print(len(train_loader))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6c236434",
   "metadata": {
    "executionInfo": {
     "elapsed": 7,
     "status": "ok",
     "timestamp": 1684414638807,
     "user": {
      "displayName": "RUIYING CHEN",
      "userId": "09384040230747805046"
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     "start_time": "2023-05-24T03:29:44.697117",
     "status": "completed"
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    "tags": []
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   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "7766ed86",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-05-24T03:29:44.716807Z",
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     "shell.execute_reply": "2023-05-24T03:29:44.728050Z"
    },
    "executionInfo": {
     "elapsed": 7,
     "status": "ok",
     "timestamp": 1684414638808,
     "user": {
      "displayName": "RUIYING CHEN",
      "userId": "09384040230747805046"
     },
     "user_tz": -480
    },
    "id": "xKlx2mCxlE02",
    "papermill": {
     "duration": 0.02143,
     "end_time": "2023-05-24T03:29:44.730763",
     "exception": false,
     "start_time": "2023-05-24T03:29:44.709333",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "from tqdm import tqdm\n",
    "def train(epoch):\n",
    "    net.train()\n",
    "    # Loop over each batch from the training set\n",
    "    train_tqdm = tqdm(train_loader, desc=\"Epoch \" + str(epoch))\n",
    "    for batch_idx, (data, target) in enumerate(train_tqdm):\n",
    "        # Copy data to GPU if needed\n",
    "        data = data.to(device)\n",
    "        target = target.to(device)\n",
    "        # Zero gradient buffers\n",
    "        optimizer.zero_grad()\n",
    "        # Pass data through the network\n",
    "        output = net(data)\n",
    "        # Calculate loss\n",
    "        loss = criterion(output, target)\n",
    "        # Backpropagate\n",
    "        loss.backward()\n",
    "        # Update weights\n",
    "        optimizer.step()  # w - alpha * dL / dw\n",
    "        train_tqdm.set_postfix({\"loss\": \"%.3g\" % loss.item()})\n",
    "\n",
    "def validate(lossv,top1AccuracyList,top5AccuracyList):\n",
    "    net.eval()\n",
    "    val_loss = 0\n",
    "    top1Correct = 0\n",
    "    top5Correct = 0\n",
    "    for index,(data, target) in enumerate(test_loader):\n",
    "        data = data.to(device)\n",
    "        labels = target.to(device)\n",
    "        outputs = net(data)\n",
    "        val_loss += criterion(outputs, labels).data.item()\n",
    "        _, top1Predicted = torch.max(outputs.data, 1)\n",
    "        top5Predicted = torch.topk(outputs.data, k=5, dim=1, largest=True)[1]\n",
    "        top1Correct += (top1Predicted == labels).cpu().sum().item()\n",
    "        label_resize = labels.view(-1, 1).expand_as(top5Predicted)\n",
    "        top5Correct += torch.eq(top5Predicted, label_resize).view(-1).cpu().sum().float().item()\n",
    "\n",
    "    val_loss /= len(test_loader)\n",
    "    lossv.append(val_loss)\n",
    "\n",
    "    top1Acc=100*top1Correct / len(test_loader.dataset)\n",
    "    top5Acc=100*top5Correct / len(test_loader.dataset)\n",
    "    top1AccuracyList.append(top1Acc)\n",
    "    top5AccuracyList.append(top5Acc)\n",
    "    \n",
    "    print('\\nValidation set: Average loss: {:.4f}, Top1Accuracy: {}/{} ({:.0f}%) Top5Accuracy: {}/{} ({:.0f}%)\\n'.format(\n",
    "        val_loss, top1Correct, len(test_loader.dataset), top1Acc,top5Correct, len(test_loader.dataset), top5Acc))\n",
    "#     accuracy = 100. * correct.to(torch.float32) / len(testloader.dataset)\n",
    "#     accv.append(accuracy)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "3b1d3630",
   "metadata": {
    "execution": {
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     "shell.execute_reply": "2023-05-24T04:58:59.676362Z"
    },
    "id": "pTWz8B3Dl9gu",
    "outputId": "b7c77e1e-40cc-417b-f5de-7467800712dd",
    "papermill": {
     "duration": 5354.943506,
     "end_time": "2023-05-24T04:58:59.680431",
     "exception": false,
     "start_time": "2023-05-24T03:29:44.736925",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 0: 100%|██████████| 5000/5000 [04:12<00:00, 19.80it/s, loss=3.36]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Validation set: Average loss: 3.7212, Top1Accuracy: 1363/10000 (14%) Top5Accuracy: 3756.0/10000 (38%)\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 1: 100%|██████████| 5000/5000 [04:03<00:00, 20.50it/s, loss=3.97]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Validation set: Average loss: 3.3701, Top1Accuracy: 1979/10000 (20%) Top5Accuracy: 4562.0/10000 (46%)\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 2: 100%|██████████| 5000/5000 [04:00<00:00, 20.76it/s, loss=2.91]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Validation set: Average loss: 3.0597, Top1Accuracy: 2473/10000 (25%) Top5Accuracy: 5414.0/10000 (54%)\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 3: 100%|██████████| 5000/5000 [04:02<00:00, 20.61it/s, loss=3.23]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Validation set: Average loss: 2.9854, Top1Accuracy: 2671/10000 (27%) Top5Accuracy: 5696.0/10000 (57%)\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 4: 100%|██████████| 5000/5000 [04:07<00:00, 20.23it/s, loss=2.42]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Validation set: Average loss: 2.7556, Top1Accuracy: 3105/10000 (31%) Top5Accuracy: 6143.0/10000 (61%)\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 5: 100%|██████████| 5000/5000 [04:06<00:00, 20.27it/s, loss=2.35]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Validation set: Average loss: 2.6881, Top1Accuracy: 3238/10000 (32%) Top5Accuracy: 6322.0/10000 (63%)\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 6: 100%|██████████| 5000/5000 [04:03<00:00, 20.53it/s, loss=2]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Validation set: Average loss: 2.6224, Top1Accuracy: 3475/10000 (35%) Top5Accuracy: 6475.0/10000 (65%)\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 7: 100%|██████████| 5000/5000 [04:05<00:00, 20.34it/s, loss=2.82]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Validation set: Average loss: 2.5940, Top1Accuracy: 3501/10000 (35%) Top5Accuracy: 6530.0/10000 (65%)\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 8: 100%|██████████| 5000/5000 [04:09<00:00, 20.07it/s, loss=2.03]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Validation set: Average loss: 2.5746, Top1Accuracy: 3630/10000 (36%) Top5Accuracy: 6669.0/10000 (67%)\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 9: 100%|██████████| 5000/5000 [04:02<00:00, 20.62it/s, loss=3.01]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Validation set: Average loss: 2.4515, Top1Accuracy: 3784/10000 (38%) Top5Accuracy: 6875.0/10000 (69%)\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 10: 100%|██████████| 5000/5000 [04:00<00:00, 20.77it/s, loss=4.06]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Validation set: Average loss: 2.4517, Top1Accuracy: 3947/10000 (39%) Top5Accuracy: 6848.0/10000 (68%)\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 11: 100%|██████████| 5000/5000 [04:05<00:00, 20.36it/s, loss=2.2]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Validation set: Average loss: 2.4597, Top1Accuracy: 3855/10000 (39%) Top5Accuracy: 6880.0/10000 (69%)\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 12: 100%|██████████| 5000/5000 [04:04<00:00, 20.47it/s, loss=2.5]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Validation set: Average loss: 2.4467, Top1Accuracy: 3900/10000 (39%) Top5Accuracy: 6950.0/10000 (70%)\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 13: 100%|██████████| 5000/5000 [04:04<00:00, 20.49it/s, loss=2.35]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Validation set: Average loss: 2.4243, Top1Accuracy: 3987/10000 (40%) Top5Accuracy: 6960.0/10000 (70%)\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 14: 100%|██████████| 5000/5000 [04:02<00:00, 20.58it/s, loss=1.34]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Validation set: Average loss: 2.5042, Top1Accuracy: 4029/10000 (40%) Top5Accuracy: 6961.0/10000 (70%)\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 15: 100%|██████████| 5000/5000 [04:06<00:00, 20.28it/s, loss=2.43]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Validation set: Average loss: 2.4679, Top1Accuracy: 3993/10000 (40%) Top5Accuracy: 6966.0/10000 (70%)\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 16: 100%|██████████| 5000/5000 [04:07<00:00, 20.21it/s, loss=2.11]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Validation set: Average loss: 2.4152, Top1Accuracy: 4176/10000 (42%) Top5Accuracy: 7105.0/10000 (71%)\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 17: 100%|██████████| 5000/5000 [03:54<00:00, 21.30it/s, loss=1.18]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Validation set: Average loss: 2.4905, Top1Accuracy: 4125/10000 (41%) Top5Accuracy: 7083.0/10000 (71%)\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 18: 100%|██████████| 5000/5000 [03:58<00:00, 21.00it/s, loss=1.71]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Validation set: Average loss: 2.5944, Top1Accuracy: 4096/10000 (41%) Top5Accuracy: 7067.0/10000 (71%)\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 19: 100%|██████████| 5000/5000 [03:59<00:00, 20.85it/s, loss=1.33]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Validation set: Average loss: 2.5366, Top1Accuracy: 4205/10000 (42%) Top5Accuracy: 7081.0/10000 (71%)\n",
      "\n",
      "Finished Training\n"
     ]
    }
   ],
   "source": [
    "lossv = []\n",
    "top1AccuracyList = []   # top1准确率列表\n",
    "top5AccuracyList = []   # top5准确率列表\n",
    "max_epoch = 20\n",
    "for epoch in range(max_epoch):  # loop over the dataset multiple times\n",
    "    train(epoch)\n",
    "    with torch.no_grad():\n",
    "        validate(lossv,top1AccuracyList,top5AccuracyList)\n",
    "        \n",
    "print('Finished Training')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "a55c6350",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-05-24T04:59:20.948872Z",
     "iopub.status.busy": "2023-05-24T04:59:20.948483Z",
     "iopub.status.idle": "2023-05-24T04:59:20.953345Z",
     "shell.execute_reply": "2023-05-24T04:59:20.952336Z"
    },
    "executionInfo": {
     "elapsed": 22810,
     "status": "ok",
     "timestamp": 1684424352765,
     "user": {
      "displayName": "RUIYING CHEN",
      "userId": "09384040230747805046"
     },
     "user_tz": -480
    },
    "id": "Hag0zV9cmHm_",
    "outputId": "1434ac5c-f76c-4637-9223-e6955c942441",
    "papermill": {
     "duration": 10.413983,
     "end_time": "2023-05-24T04:59:20.955538",
     "exception": false,
     "start_time": "2023-05-24T04:59:10.541555",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# from google.colab import drive\n",
    "# drive.mount('/content/drive')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "d1648cd0",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-05-24T04:59:42.656191Z",
     "iopub.status.busy": "2023-05-24T04:59:42.655842Z",
     "iopub.status.idle": "2023-05-24T04:59:43.367223Z",
     "shell.execute_reply": "2023-05-24T04:59:43.366299Z"
    },
    "id": "Z1-D3LkhvzDq",
    "papermill": {
     "duration": 11.871411,
     "end_time": "2023-05-24T04:59:43.369321",
     "exception": false,
     "start_time": "2023-05-24T04:59:31.497910",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'validation top5 accuracy')"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 500x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 500x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 500x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "# 绘制曲线\n",
    "plt.figure(figsize=(5, 3))\n",
    "plt.plot(np.arange(1, max_epoch + 1), lossv)\n",
    "plt.title('validation loss')\n",
    "\n",
    "plt.figure(figsize=(5,3))\n",
    "plt.plot(np.arange(1, max_epoch + 1), top1AccuracyList)\n",
    "plt.title('validation top1 accuracy')\n",
    "\n",
    "plt.figure(figsize=(5,3))\n",
    "plt.plot(np.arange(1, max_epoch + 1), top5AccuracyList)\n",
    "plt.title('validation top5 accuracy')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4deb369b",
   "metadata": {
    "id": "fyWGvlbHup5w",
    "papermill": {
     "duration": 10.453858,
     "end_time": "2023-05-24T05:00:04.292481",
     "exception": false,
     "start_time": "2023-05-24T04:59:53.838623",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.10"
  },
  "papermill": {
   "default_parameters": {},
   "duration": 5463.428574,
   "end_time": "2023-05-24T05:00:17.933335",
   "environment_variables": {},
   "exception": null,
   "input_path": "__notebook__.ipynb",
   "output_path": "__notebook__.ipynb",
   "parameters": {},
   "start_time": "2023-05-24T03:29:14.504761",
   "version": "2.4.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
